Sensor Calibration Using Americium-Aluminum in the NEWS-G Experiment at SNOLAB
Bibliographic record
Abstract
The new experiments with spheres - gas (NEWS-G) experiment is an international collaboration working with spherical gas detectors. The detector works by filling a sphere with a concentrated gas and placing a multi-anode sensor inside the sphere. When a particle interacts with the gas, the gas gets ionized, and an ionization electron is created. When a high voltage is applied to the anodes on the sensor, the ionization electron is attracted to the anodes. Once the electron reaches an anode, it can be detected. Ideally, a light dark matter particle would interact with the gas and be detected. In this experiment, the sensor at NEWS-G in SNOLAB had broken and needed to be replaced. To replace the broken sensor, the experiment required that a new sensor be calibrated to ensure it was working properly. The new sensor was calibrated at Queen's University using an americium-aluminum source which emitted X-rays of known energy levels. By placing the source on different parts of the sphere, each anode can be tested, and the results from each anode can be analyzed. After reviewing all the collected data, it was found that a few of the anodes had gain differences, likely due to the anode not being centered on the wire to which it was connected. All of the anodes detected the source, seeing similar results, and it was determined that the sensor was functional enough to be used in the NEWS-G experiment at SNOLAB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".